How Online Communities Support Human Values. Michael Leitner, Peter Wolkerstorfer, Manfred Tscheligi. CURE - Center for Usability. Research & Engineering.
Proceedings: NordiCHI 2008, October 20-22, 2008
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How Online Communities Support Human Values Michael Leitner, Peter Wolkerstorfer, Manfred Tscheligi CURE - Center for Usability Research & Engineering Hauffgasse 3-5 1110 Vienna 0043 (0)1 743 5451
{michael.leitner | wolkerstorfer | tscheligi}@cure.at ABSTRACT With our work we refer to value-sensitive and value-centered design approaches to answer the question “why” people join online communities. We conducted qualitative semi-structured Laddering interviews with 21 participants to identify relevant behavior motives for the use of online communities. We identified friendship, self-reflection and information purposes as the most relevant motives. Further, we demonstrate that in the users’ experience online communities serve as information pools of social networks used for self-identification and selfreflection.
Categories and Subject Descriptors J.4 [Social and behavioral sciences] Economics, Psychology, sociology
General Terms Design, Human Factors
Keywords Means-end chains, Laddering interviews, user experience, human values, online communities, value-centered design
1. INTRODUCTION In the last years online communities have become very popular. Sites like YouTube, Flickr and Myspace are examples of very powerful platforms used by thousands of people to publish self produced multimedia content, to create online social networks and to maintain their personal online profile. At first sight, a lot of content published targets on entertainment and fun. A closer look learns that the information published or shared is in many cases very personal. Music, photos, personal profiles and preferences imply expression of oneself and personal interests and values. To answer the question why online communities are so popular one has to consider whether “tools” offer more than simple functionality alone. Joinson e.g. found that people use Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. NordiCHI 2008: Using Bridges, 18-22 October, Lund, Sweden Copyright 2008 ACM ISBN 978-1-59593-704-9. $5.00
communities to “observe” others virtually and to present themselves online – beside other activities [5]. On the basis of their results the question comes up why people do so and how they experience their online behavior. Hence, in this study we go one step further and present underlying behavior motives in online communities and show how these are tied to human values. With this study we refer to Friedman’s work on valuesensitive design. She states that every system (or product) adheres to certain values. Cockton coined the term valuecentered design, which as well refers to the idea of value adding systems and tools [2],[3],[4]. Our present work will show which human values and underlying behavior motives refer to online communities. Hence, by showing which human values are tied to online communities its popularity may be explained clearer. In contrast to actual studies answering “what” people do, we go one step further and state “why” people join online communities. We applied a method called “Laddering Interviews” to identify human values and experiences which people relate to online communities. This interview technique refers to Gutman’s Means-End theory [8]. Interview results describe relations between basic attributes (e.g. sharing content) and basic human values (e.g. friendship) in so-called means-end chains, which consist of several abstract consequences (=behavior motives). In the present study we consider online communities that offer different networking, publishing and sharing tools, without focusing on particular communities or topics.
2. RELATED WORK What members of online communities do online has been investigated by different studies. Joinson gathered a snapshot of Facebook users in two studies conducted with 378 users [5]. Respondents indicated that they use Facebook to keep people under virtual surveillance. The users were eager to see what old contacts and friends were up to, how they behave and how they look (how they have changed). On the other hand Facebook was used as a self-presentation tool, building social capital and maintaining contact with distant friends. However, the usage patterns are subject to different conditions and to the users’ intentions. Similarly, Lampe et al. have investigated the users’ motives for using online communities: either to find new friends or to learn more about people they initially met offline [6]. As mentioned these results reflect “what” people do in online communities, with our work we will rather bring more insights to answer “why” people are acting online in communities.
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Proceedings: NordiCHI 2008, October 20-22, 2008
Cockton introduces the term “value-centered design”. Overall this concept refers to the early work of Friedman, who coined the term value-sensitive design [4]. According to Cockton systems are without worth when they fail to deliver values. In contrast products are considered worthy if they give users expected or even unexpected values [2],[3]. With our work we refer to these concepts seeking to provide a framework indicating motives and values in the realm of online communities. According to value-centered design designers may use this input for refining and designing gainful features of online communities. The Means-End theory and the “Laddering” method have been used in computer science before, e.g. to investigate user requirements and to identify usage motives in the realm of web and mobile applications [1],[7],[10]. In our experience “Laddering” is a powerful technique able to extract values and users’ experience in different realms.
3. METHODOLOGY We conducted semi-structured, qualitative, “laddering” interviews with 21 participants. We chose this method as it was designed to extract relevant means-end chains and values related to a product or service. We had already used the method in the realm of mobile multimedia and experienced it as a powerful tool for the extraction of particular behaviors and motives [7]. In the actual study all involved subjects were members of online communities and had an active personal community profile. Respondents accessed their profile at least once a week, regularly updated their profile information or published content. Respondents were in the age of 20 to 35 (mean 24.5). All subjects lived in urban areas. 12 out of 21 respondents were female, 9 were male. 9 subjects were employed, 12 were students.
3.1 Means-End Chains and Laddering Interviews Laddering interviews are a qualitative research technique referring to Gutman’s Means-End Theory [8]. It was established in order to identify important meanings which consumers associate with products. The Means-End theory distinguishes three abstraction levels of meanings: attributes, consequences and values. First, attributes are on equal terms with characteristics of a product. Consequences are more abstract and describe possibilities offered by the product’s characteristics (a product enabling the user to execute particular actions). Lastly, values represent abstract meanings, motivational constructs and beliefs that are directly tied to emotions [8]. As a result the Means-End theory builds linkages between the different levels of abstraction and shows why attributes and consequences are important to users. At the interview each respondent argues why particular product are important to her/him constructing linkages between attributes, consequences and values. The result of a single interview is one (or more) Ladder(s). In analysis the total amount of individual respondent’s Ladders is taken and reviewed for similarities. Similar Ladders are represented by a single means-end chain. Finally, means-end chains are displayed in the Hierarchical Value Map (Figure 1). Laddering focuses on product characteristics that are important to the user and have an explorative character. The interviewer tries to find out why these characteristics are
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important to the interviewee by asking “Why is this important to you?” This leads to an increased level of abstraction. Attributes will lead to consequences and finally into values. The technique’s name “Laddering” refers to the consecutive rise of abstraction. The following section describes the standard analysis process of Laddering interviews.
3.2 Study and Interview Data 3.2.1 Interview conduction (Laddering) Each participant was asked to name one ore more reasons why she/he is member of an online community. For instance participant 11 stated: “I’m in an online community as I want to get information on particular style of music”. This is the attribute the interview starts with. The interviewer continues to ask, “why is this important to you”, trying to raise the abstraction level. The respondent answers and formulates the first consequence: “Because I want to know what’s going on”. On the basis of this consequence the interviewer continues asking “why is this important to you”, forcing the participant to formulate the next consequence. Participant 11 answered: “Being up to date I may talk to other people about my hobby and interests”. The interviewer continues, “why is this important to you” and the participant answers: “Talking to other people about my hobbies makes me happy”. As the respondent expressed a value (“happiness”) the Ladder is finished with one attribute, followed by two consequences and finally one value.
3.2.2 Data Analysis All interviews together provided us with 26 Ladders (more than one Ladder is possible for each participant). For further analysis the data is analyzed and grouped in so called Content Codes. This implies that attributes, consequences and values referring to the same meaning are summarized within one Content Code1. The gathered Content Codes are described in Table 1. Respondents’ Ladders are then categorized using these Content Codes. Given example of participant 11 in section 3.2.1 may be described as a Ladder of Content Codes in the following manner (see Table 1): 4 (information purposes / opinion exchange) – 6 (curiosity and interests) – 13 (communicate over certain topic) – C (pleasure and happiness). Similarly, each of the 26 Ladders was classified by Content Codes. The categorized total amount of 26 Ladders is summarized by the so called “Implication Matrix” (Table 2). This matrix indicates the number of direct and indirect relations between the particular Content Codes in the total expenditure of Ladders. Two types of relations are possible: Direct relations indicate that a value or consequences is a direct subsidence of given attribute or consequence. In the given example stated in the text above Content Codes 4 and 6 show a direct relation as 6 -13 and 13 – C do. In contrast 4 and C are indirectly related to each other.
1
For instance: “I use online communities to stay in touch with friends” mentioned by one respondent and “I use online communities because I like receiving messages from friends” by a different respondent are categorized into one Content Code. A representative name is chosen for each Content Code (see Table 1).
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Proceedings: NordiCHI 2008, October 20-22, 2008
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Table 1: Content Codes are categories summarizing similar respondent’s answers. The last column indicates the number of answers referring to each category. attribute
Content Code 5. Again, node 5/12 shows an amount of relations higher than the cut-off level. The next relevant node is 12/A. The means-end chain stops here as it reached a value. The constructed means-end chain is: “1 – 5 – 12 – A” and may be drawn to the Hierarchical Value Map (HVM). In this manner the whole HVM is constructed revising all relevant nodes of the Implication Matrix. The HVM is a visual summary of all relevant relations and important Content Codes represented by means-end chains (Figure 1). Note that Content Codes with no relevant relations are not further considered. Further details on the analysis process are presented in [7] and [8].
Nr.
1 communication purposes
10
2 create new relationships
5
3 share content with others
3
4 information purposes / opinion exchange
8
consequences 5 overcome time and space limitations
5
6 curiosity and interests
8
7 know what other people do (learn from others)
8
8 compare oneself with others
1
9 take part of peoples life and share experiences
3
10 present and express oneself
3
11 be informed and up to date
7
12 strengthen and retain friendship
4
13 communicate over certain topic
8
14 entertain and inform other people/friends
2
15 avoid boring and lonely time
1
16 helps to understand people better
4
4. RESULTS: Hierarchical Value Map The main result is summarized in Figure 1 showing the Hierarchical Value Map (HVM) and all relevant means-end chains identified. The most important attributes, i.e. the most obvious characteristics of online communities, observed were: • • •
In contrast “share content with others” was mentioned by respondents, but with no significant occurrence. Analyzing the attributes, consequences and values linked to each other we noticed that these means-end chains are tied to particular topics. Summarizing, we identified the following three thematic pillars of online communities:
values (taken from Rockeach’s Value List) [9] A friendship (social positioning)
13
B broadminded and intellectual
9
C pleasure and happiness
4
• •
Once having constructed the Implication Matrix one is able to identify the most important nodes of the given Ladders to construct final means-end chains. In Table 2 the Implication Matrix indicates all relations higher than 2 (marked in grey). This is the cut-off level we chose for the given study. This means that relations are considered as relevant if at least two Ladders named by respondents show a direct relation.
•
5 4.0
6
7
8
9
1
1.1
0.1
0.1
1.1
1.0 1.1 0.4 1.0 1.0 0.1 0.1 0.7
2
1.0
1.1
3.1
0.1 1.0
0.1 0.2
3 4 5
0.1 0.1
2.1
2.1
1.2
1.1
0.2
2.0
2.0
6 7
1.0
11
12
13
14
15
16
A
0.2 1.0 1.0
0.1
0.4
0.1
0.1 0.4
0.3
0.1
1.2
0.1
0.2
1.0
2.1
0.3
0.2
1.0
3.0 1.1
2.4
8
1.0
9
1.0
1.0
0.1
10
1.0 1.1
1.0 2.1
11
1.0
12
1.0
0.1
13 1.0
0.1
0.1
14 0.1
C 0.2
0.1
1.0 2.0 1.0
B 0.1
0.2
0.1 2.1 0.1 2.4 0.1
The communication chain: People want to be informed and to communicate upon certain topics. The self-reflective chain: People want to learn from other people for self-reflecting reasons. The friendship chain: People want to overcome certain space limitations to maintain and strengthen relationships.
The most relevant finding is the self-reflective chain. An interpretation might be that people use online communities to position themselves in society. This would explain the surveillance activities Joinson reports [5]. Online communities serve as an information pool, which people use as a social “state of the art”. As represented in the HVM’s self-reflective
To analyze one start with the first row in the application matrix observing nodes with a number of relations higher than the cut-off level. For instance, in Table 2 junction 1/5 shows a number of 4 direct relations. Further, one skips the row to 10
Communication purposes Create new relationships Opinion and information exchange
1.0
0.1
2.0
1.0
0.4
2.0
3.1 1.0 1.0 1.1
1.3
1.0 1.0
0.2
15
1.0
16
2.0
505
0.1 1.0
2.1
2.0
1.0
Table 2: The Implication Matrix: Relevant relations are marked in grey and show a number of direct relations higher than the chosen cut-off level of 2. The given numbers indicates the amount of Ladders showing the same particular relation between these Content Codes. For instance node 1/5 indicates that in the sum of 26 Ladders 4 Ladders showed a direct relation between attribute “communication purposes” (Content Code 1) and the consequence “overcome time and space limitations” (Content Code 5). (Numbers in front of the period stand for direct relations, numbers after the period show the indirect relations).
Proceedings: NordiCHI 2008, October 20-22, 2008
Short Papers Figure 1: Hierarchical Value Map (HVM): The picture shows the resulting attributes, consequences (behavior motives) and finally related human values. At the bottom “attributes” indicate, why people use online communities. Consequences refer to more abstract reasons of use. At the end of each means-end chain a value is indicated. For instance, the self-reflective chain starts with Content Code (2) “Create New Relationships”. Further this is important to people as they (7) “Know what other people do” as they then may (16) “understand oneself and other people” better. Finally this is important as people may want to maintain (A) “friendship” as well as to be (B) “broad minded and selfdirective”.
chain, online communities support opinion and information exchange enabling people to be informed and finally selfdirective. According study’s results online communication is used to strengthen and retain friendship. This refers to Lampe’s and Joinsons findings on people’s ambition in regaining old friends and finding new people online [5],[6]. These behaviours are related to two main human values, which represent the HVM’s top level: • •
Broad minded and the ability to be self-directive and Friendship
In other words, people use online communities because they support/enable (a) friendship and (b) people’s ambition of being an open-minded person that is (c) up to date and well informed and (d) able to make self-reflected decisions.
5. CONCLUSION and DISCUSSION We conducted 21 Laddering interviews to extract relevant behavior motives and human values adhering to online communities. We refer to the concepts of value-sensitive and value-centered design and provide a framework of human values and relevant behavior motives for the given realm in order to provide insight into people’s online behavior. The results reflect the user’s experience and may be used to design and refine attributes of online communities keeping in mind what users expect. Although the sample of 26 Ladders is small, we believe the presented results to be significant showing the most relevant means-end chains. Further research could result in slight changes of the HVM. However, we are sure that the method and actual results show good insight in user experience factors of online communities.
6. REFERENCES
[2] Cockton, G.; Value-centred HCI. Proceedings of the third Nordic conference on Human computer interaction, NewYork, NY, USA, 2004 [3] Cockton, G.; A development framework for valuecentred design. Paper presented at CHI ’05: CHI ’05 extended abstracts on Human factors in computing systems, New York, NY, USA, 2005 [4] Friedman, B.; Value-sensitive design. Interactions, 3(6) 16-23, 1996 [5] A.N. Joinson. ‘Looking at’, ‘Looking up’ or ‘Keeping up with’ People? Motives and Uses of Facebook. In Proceedings of CHI 2008, April 5-10, 2008. Florence, Italy [6] C.A. Lampe. N. Ellison. C. Steinfield. A familiar face(book): profile elements as signals in an online social network. ). CHI '07. ACM, New York, NY, 435-444. San Jose, California, USA, April 28 - May 03 [7] Leitner, M, Wolkerstorfer P, Sefelin R. Tscheligi, M, Mobile Multimedia: Identifying User Values Using the Means-End Theory; to appear at MobileHCI 2008, September 2–5, 2008, Amsterdam, the Netherlands. [8] Reynolds, Thomas J. and Olson, Jerry C.; Lawrence, Understanding Consumer Decision Making, The meansend approach to marketing and advertising strategy; Erlbaum Associates [9] Rokeach, Milton, The nature of human values. New York, Free Press [10] Subramony, D.P.; Introducing a" Means-End" Approach to Human-Computer Interaction: Why Users Choose Particular Web Sites Over Others; Association for the Advancement of Computing in Education (AACE), 2002
[1] Chao-Min Chiu; Applying means-end chain theory to eliciting system requirements and understanding users perceptual orientations; Inf. Management; volume42, Nr. 3, 2005, pages 455-468; Elsevier Science Publishers B. V.; Amsterdam, The Netherlands, The Netherlands
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